La reconstruction faciale permet d’approximer un visage sur la base d’un crâne, lorsque des restes humains sont retrouvés. En science forensique, elle est l’un des outils utilisés dans un but d’identification post-mortem. Les procédures actuelles d’approximation ne sont pas standardisées et constamment revisitées. Il est également possible d’obtenir des prédictions du phénotype d’un individu (caractères physiques apparents) à partir de son ADN, qui pourraient être ajoutées aux reconstructions pour maximiser les chances de reconnaissance. Cette recherche vise à étudier l’approximation faciale à des fins de reconnaissance et l’apport du phénotypage par l’ADN à cette pratique. Le crâne et le relevé biologique d’un donneur du laboratoire d’anatomie de l’UQTR ont été utilisés. Six praticiens ont approximé son visage à partir d’une copie de son crâne et de ses données phénotypiques et anthropologiques. Les résultats corroborent qu’il existe un manque de standardisation des méthodes et techniques, menant à différents résultats selon le praticien. Des tests de reconnaissance et de ressemblance ont été effectués à l’aide d’un échantillon de 46 participants. Malgré la variabilité des approximations, elles ont toutes été reconnues au moins une fois lors des tests de reconnaissance, soulevant la possibilité que la reconnaissance d’un visage est idiosyncratique. Les caractéristiques qui semblent influencer davantage la reconnaissance sont la forme, la taille et la position des yeux, de la bouche et du nez. Finalement, au regard des incertitudes propres à la méthode et du rôle investigatif de l’approximation faciale, il est recommandé que le phénotypage accompagne l’accompagne par écrit. / Facial reconstruction is a process by which a face is approximated from a skull when human remains are found. In forensic science, it is one of the many tools used for the purpose of post-mortem identification. The current approximation procedures are not standardized and are always revisited. Nowadays, it is possible to obtain phenotype (apparent physical traits) predictions from an individual’s DNA. These predictions could be added to facial approximations to maximize the chances of recognition. This research aims to study facial approximation for recognition purposes and the plus-value of DNA phenotyping to facial approximation. The skull and biological material from one donor of the UQTR’s anatomy laboratory were used. Six practitioners approximated the donor’s face using a copy of his skull, and phenotyping and anthropological data. The results corroborated the lack of standardization regarding the approximating methods and techniques, which leads to different resulting approximations depending on the practitioner. Recognition and resemblance tests were carried out with a sample of 46 participants. Despite the wide variability of the approximations, they were all recognized at least once during the recognition tests, raising the possibility that the recognition of a face is idiosyncratic. The characteristics that seemed the most important to recognition were the shape, size and position of the eyes, the mouth, and the nose. Finally, with regard to the uncertainties specific to the method and the final investigative role of facial approximation, phenotyping would benefit in feeding a spoken portrait.

Die Phänotypisierung von Cytochrom P450 (CYP) und Leberfunktionstests sind wichtige Methoden in der Klinik. Die Methoden nutzen die Pharmakokinetik (PK) von Testsubstanzen und ihren Metaboliten, um Einblicke in die Stoffwechselkapazität der Leber und in die Aktivität von Enzymen und Transportern zu gewinnen. Die Leberfunktionstests werden nicht nur von zahlreichen Proband:innenmerkmalen, sondern auch von den Besonderheiten der Untersuchung beeinflusst. Eine zentrale Herausforderung besteht darin, die verschiedenen Faktoren, die das Ergebnis der Messungen beeinflussen, voneinander zu trennen, um ihren jeweiligen Einfluss auf das Messergebniss zu untersuchen. In dieser Arbeit wurde die Herausforderung durch Metaanalysen und physiologisch basierte Pharmakokinetik Modellierung (PBPK) angegangen. Es wurde eine offene Pharmakokinetik-Datenbank (PK-DB) entwickelt und PK-Daten für ein breites Spektrum von Testsubstanzen kuratiert. Meines Wissens enthält PK-DB derzeit den größten offenen PK-Datensatz zu Testsubstanzen. Der Datensatz ermöglichte die Identifizierung und Quantifizierung von demografischen und rassischen Bias (Geschlecht, ethnische Zugehörigkeit, Alter, Gesundheitszustand), Meldefehlern und Unstimmigkeiten in der Literatur. Auf der Grundlage der Daten wurde eine Metaanalyse der PK von Koffein im Hinblick auf verschiedene Faktoren bzgl. Leberfunktion und CYP1A2-Aktivität durchgeführt. Insbesondere wurde das vorhandene Wissen über die Auswirkungen des Rauchens, der Einnahme oraler Verhütungsmittel, verschiedener Krankheiten und Begleitmedikationen auf die PK von Koffein durch Metaanalysen und Datenintegration konsolidiert. Ebenso wurde die Messgenauigkeit der Koffeinkonzentration in Bezug auf den Messprotokol analysiert. Darüber hinaus wurde der Einfluss des CYP2D6-Polymorphismus untersucht. Hierzu wurde ein PBPK-Modell für Dextromethorphan und seine Metaboliten Dextrorphan und Dextrorphan O-Glucuronid entwickelt und mit den PK-Daten kalibriert und validiert. / Cytochrome P450 (CYP) phenotyping and dynamic liver function testing are essential methods in clinical practice. These methods utilize the pharmacokinetics (PK) of test substances and their metabolites to gain insight into the liver's metabolic capacity and the activity of enzymes and transporters. Liver function tests are not only influenced by numerous characteristics of a studied subject but also by the specifics of individual study procedures. A key challenge is to disentangle the various factors which influence the outcome of the measurements from each other to study their influence on the dynamic liver function and CYP phenotype. In this work, the challenge was addressed through meta-analysis and physiologically based pharmacokinetic modeling. As a foundation, an open pharmacokinetics database was developed and pharmacokinetics data were curated for a wide range of test substances. To my knowledge, PK-DB currently contains the largest open pharmacokinetic dataset on substances used for phenotyping and dynamical liver function testing. The dataset allowed for identifying and quantifying demographic and racial bias (sex, ethnicity, age, health), reporting errors, and inconsistencies in pharmacokinetic literature. Based on the data, a caffeine pharmacokinetics meta-analysis was conducted concerning various factors affecting liver function and CYP1A2 activity. In particular, meta-analysis and data integration solidified existing knowledge on the effects of smoking, oral contraceptives, multiple diseases, and co-medications on caffeine pharmacokinetics. Similarly, the measurement accuracy of caffeine concentration was investigated with respect to various aspects of the measurement protocol. In addition, the impact of CYP2D6 polymorphism was investigated. Therefore, a PBPK model of dextromethorphan (DXM) and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) was developed, and calibrated, and validated with pharmacokinetics data.

D’ici la fin du siècle, les prévisions climatiques prévoient une diminution de la quantité et de la régularité des pluies s’accompagnant d’une augmentation du risque de sècheresse en Europe et dans de nombreuses régions du monde. La création de nouvelles variétés de maïs plus tolérantes au stress hydrique est un levier indispensable pour faire face à ces contraintes futures. L’objectif principal de cette thèse est d’approfondir les connaissances des déterminismes génétiques de la tolérance à la sècheresse chez le maïs. Pour ce faire, il est proposé de disséquer ce caractère complexe en caractères physiologiques sous-jacents dont le déterminisme génétique est a priori plus simple. L’évolution de l’indice foliaire vert (GLAI : Green Leaf Area Index) au cours du cycle de la plante, par son rôle majeur dans l’interception lumineuse, la transpiration et les échanges de CO2, est un caractère secondaire prometteur pour identifier les bases génétiques de la tolérance à la sècheresse et en améliorer la compréhension. Au cours de cette thèse, nous avons développé une méthode de phénotypage haut débit permettant d’estimer la cinétique du GLAI au champ. Cette méthode combine la caractérisation multispectrale par drone et l’utilisation d’un modèle physiologique simple de GLAI. Elle permet d’estimer la cinétique du GLAI de manière continue sur l’ensemble du cycle de la plante avec une bonne précision, tout en divisant par vingt le temps nécessaire au phénotypage. Nous avons utilisé cette méthode lors de deux essais en conditions optimales et deux essais en conditions de stress hydrique pour mesurer l’évolution du GLAI au sein d’un panel de 324 lignées issues d’une population MAGIC (Multi-parent Advanced Generation Inter-Cross). Les cinétiques estimées présentent une forte héritabilité et expliquent une part significative du rendement en conditions optimales et stressées. Afin d’identifier les bases génétiques de la cinétique du GLAI, trois approches de génétique d’association longitudinales ont été comparées : une approche univariée en deux étapes, une approche multivariée en deux étapes et une approche de régression aléatoire en une étape. Ces trois approches, couplées à la forte densité des données de génotypage disponibles (près de 8 millions de marqueurs), ont permis de révéler de nombreux QTL (Quantitative Trait Loci), dont certains colocalisent avec des QTL de rendement. Enfin, nous avons démontré que les QTL de GLAI identifiés lors de cette étude pouvaient expliquer près de 20 % de la variabilité du rendement observée dans un large réseau d’expérimentations sous stress hydrique. Ce travail fournit des méthodes qui permettront une meilleure caractérisation et une meilleure compréhension des déterminismes génétiques de la cinétique du GLAI, un caractère jusqu’ici inaccessible pour les populations de taille importante. Ce caractère présente toutes les caractéristiques requises pour améliorer l’efficacité des programmes de sélection en conditions de stress hydrique. / By the end of the century, climate forecasts predict a decrease in the quantity and regularity of rainfall with an increasing risk of drought in Europe and in many regions of the world. Breeding for more tolerant varieties will be an essential lever to face these future constraints. The main objective of this work is to characterize the genetic determinisms of drought tolerance in maize. To this aim, it is proposed to dissect this complex trait into underlying physiological traits whose genetic determinism is supposed to be simpler. Green Leaf Area Index (GLAI) dynamics throughout the plant cycle, through its major role in light interception, transpiration and CO2 exchange, is a promising secondary trait to identify and better understand the genetic basis of drought tolerance. During this thesis, we developed a high-throughput method for phenotyping maize GLAI dynamics in the field. This method combines UAV multispectral imagery and a simple GLAI model. It makes possible the estimation of the dynamics of GLAI continuously throughout the whole plant cycle with good accuracy, while reducing the phenotyping time twentyfold. This method was used in two well-watered and two water-deficient trials to characterize the GLAI dynamics of 324 lines from a MAGIC population (Multi-parent Advanced Generation Inter-Cross). The estimated dynamics have a high heritability and explain a significant part of grain yield under well-watered and water-stressed conditions. To characterize the genetic basis of GLAI dynamics, three longitudinal GWAS (Genome Wide Association Study) approaches were compared: a univariate two-step approach, a multivariate two-step approach and a random regression one-step approach. These three approaches, combined with the high density of available genotyping data (nearly 8 million markers), have revealed many QTL (Quantitative Trait Loci), some of which were co-localized with yield QTL. Finally, we demonstrated that the GLAI QTL identified in this study could explain nearly 20 % of the grain yield variability observed in a large network of water-stressed experiments. This work provides methods that will enable a better characterization and understanding of the genetic determinisms of GLAI dynamics, a trait that was out of reach in large populations until now. This trait presents all the characteristics required to improve the effectiveness of selection programs under water stress conditions.

<p dir="ltr">Detecting anomalies in spacecraft time-series data is a high priority, especially considering the harshness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Traditionally, the time-series data channels are monitored manually by domain experts, which is time-consuming. Additionally, there are thousands of channels to monitor. Machine learning methods have proven to be useful for automatic anomaly detection, but a unique model must be trained from scratch for each time-series. This thesis proposes three approaches for reducing training costs. First, a transfer learning approach that finetunes a general pre-trained model to reduce training time and the number of unique models required for a given spacecraft. The second and third approaches both use online learning to reduce the amount of training data and time needed to identify anomalies. The second approach leverages an ensemble of extreme learning machines while the third approach uses deep learning models. All three approaches are shown to achieve reasonable anomaly detection performance with reduced training costs.</p><p dir="ltr">Measuring the phenotypes, or observable traits, of a plant enables plant scientists to understand the interaction between the growing environment and the genetic characteristics of a plant. Plant phenotyping is typically done manually, and often involves destructive sampling, making the entire process labor-intensive and difficult to replicate. In this thesis, we use image processing for characterizing two different disease progressions. Tar spot disease can be identified visually as it induces small black circular spots on the leaf surface. We propose using a Mask R-CNN to detect tar spots from RGB images of leaves, thus enabling rapid non-destructive phenotyping of afflicted plants. The second disease, bacteria-induced wilting, is measured using a visual assessment that is often subjective. We design several metrics that can be extracted from RGB images that can be used to generate consistent wilting measurements with a random forest. Both approaches ensure faster, replicable results, enabling accurate, high-throughput analysis to draw conclusions about effective disease treatments and plant breeds.</p>

<p>  </p> <p>Hyperspectral Imaging is one of the most popular technologies in plant phenotyping due to its ability to predict the plant physiological features such as yield biomass, leaf moisture, and nitrogen content accurately, non-destructively, and efficiently. Various kinds of hyperspectral imaging systems have been developed in the past years for both greenhouse and field phenotyping activities. Developing the plant physiological prediction model such as relative water content (RWC) using hyperspectral imaging data requires the adoption of machine learning-based calibration techniques. Convolutional neural networks (CNNs) have been known to automatically extract the features from the raw data which can lead to highly accurate physiological prediction models. Once a reliable prediction model has been developed, sharing that model across multiple hyperspectral imaging systems is very desirable since collecting the large number of ground truth labels for predictive model development is expensive and tedious. However, there are always significant differences in imaging sensors, imaging, and environmental conditions between different hyperspectral imaging facilities, which makes it difficult to share plant features prediction models. Calibration transfer between the imaging systems is critically important. In this thesis, two approaches were taken to address the calibration transfer from the greenhouse to the field. First, direct standardization (DS), piecewise direct standardization (PDS), double window piecewise direct standardization (DPDS) and spectral space transfer (SST) were used for standardizing the spectral reflectance to minimize the artifacts and spectral differences between different greenhouse imaging systems. A linear transformation matrix estimated using SST based on a small set of plant samples imaged in two facilities reduced the root mean square error (RMSE) for maize physiological feature prediction significantly, i.e., from 10.64% to 2.42% for RWC and from 1.84% to 0.11% for nitrogen content. Second, common latent space features between two greenhouses or a greenhouse and field imaging system were extracted in an unsupervised fashion. Two different models based on deep adversarial domain adaptation are trained, evaluated, and tested. In contrast to linear standardization approaches developed using the same plant samples imaged in two greenhouse facilities, the domain adaptation extracted non-linear features common between spectra of different imaging systems. Results showed that transferred RWC models reduced the RMSE by up to 45.9% for the greenhouse calibration transfer and 12.4% for a greenhouse to field transfer. The plot scale evaluation of the transferred RWC model showed no significant difference between the measurements and predictions. The methods developed and reported in this study can be used to recover the performance plummeted due to the spectral differences caused by the new phenotyping system and to share the knowledge among plant phenotyping researchers and scientists.</p>

<p dir="ltr">Urban agriculture has grown in popularity in recent decades, due to its ability to provide access to healthy fruits and vegetables in urban zones, as well as its importance in fostering knowledge of agriculture within communities. However, urban agriculture may struggle with unique challenges due to its proximity to urban and industrial activities, such as food safety risks due to toxic heavy metals and metalloids which may be present in urban soils in high concentrations. Heavy metals and metalloids (HM) like arsenic, cadmium, and lead are absorbed by plants from the soil, and may accumulate in the plants’ edible tissues, which are consumed by humans. Carrot (<i>Daucus carota</i> L.), in particular, hyperaccumulates these toxic heavy metals in its edible taproots, leading to food safety risks on urban farms.</p><p dir="ltr">One potential way to help address this challenge is to breed carrot varieties with low uptake of HM. In recent years, researchers have identified lines with high and low uptake in greenhouse trials and single location breeding nurseries. However, to be viable, these lines must consistently vary in HM across sites despite differences in environmental and management factors that can also greatly influence HM bioavailability and uptake. Moreover, screening for differences in HM uptake is time-consuming and expensive, and breeders need new tools to select among segregating breeding populations. By using on-farm participatory research as well as advanced phenotyping technologies, we investigate the viability of breeding carrots for HM uptake and the potential of new tools to advance these efforts in order to mitigate the risks on urban farms.</p><p dir="ltr">In the summers of 2021 and 2022, participatory on-farm trials were conducted to determine the HM risks on Indiana urban farms and to investigate the consistency of differences in HM uptake between carrot breeding lines taken from breeding trials (Chapter 2). Results of these trials indicated that while carrot genotype had an effect, there was still significant variability in carrot uptake of arsenic, cadmium and lead between farm sites and years. Results indicated significant differences between site-years, and carrot HM concentrations that correlated strongly with soil concentrations for that particular element. However, there were some site-years with low soil HM content and other soil factors expected to reduce uptake such as pH and phytoavailable zinc concentrations (such as site-year H), that had high carrot HM content. There were significant differences in carrot cadmium (Cd) and arsenic (As) content between carrot breeding lines. For instance, breeding line 3271 had a high As average concentration but low Cd average concentration, while breeding lines 6220 and 2327 had low As and high Cd concentrations. We identify the possibility of other mediating factors, such as uptake of antagonistic micronutrients, or microbe-assisted HM uptake and amelioration that need further attention.</p><p dir="ltr">In the fall of 2022, a study was conducted to investigate the possibility of using phenotyping technologies such as RGB and hyperspectral imaging to detect Cd stress in carrot and attempt to predict uptake (Chapter 3). RGB (red green blue) is a digital color model in which cameras can capture important visual cues compiled from information about each pixel. Hyperspectral imaging uses cameras to capture wavelengths beyond the visible spectrum, which can detect plant stress indicators like increased anthocyanin content for specific environmental stresses. Results of this trial were useful, with some time points and indices noting differences between carrot lines. For instance, RGB factors hue and fluorescence as well as hyperspectral reflectance plots and vegetative indices swirNDVI and ANTH were the most diagnostic. Breeding lines 6636 and 8503 showed the greatest separation between Cd treated and control carrots in imaging indices. However, further studies will be needed to optimize this approach for breeding programs.</p><p dir="ltr">This research demonstrates that growing carrots on most urban farms in Indiana is safe. The studies also provide further evidence that it will be possible to help lower food safety risks by selecting carrot varieties with low HM uptake, and phenotyping can help to advance these efforts. At the same time, new research to understand how soil factors such as microbiomes influence HM bioavailability and uptake on urban farms are also needed to further reduce potential risks. In the meantime, farmers should continue to test their soil for HM and take appropriate actions to reduce risks such as using raised beds and soil amendments that can bind metals like biochar. Consumers should also continue to wash and peel their carrots before consumption, as well as consume a balanced diet with a diverse set of vegetables and other crops.</p>

La réponse immunitaire est régulée spatialement et temporellement. Les cellules immunitaires font partie d’une plus grande communauté de populations cellulaires interconnectées qui coordonnent leurs actions par la signalisation intercellulaire. Suivant une blessure hépatique, la distribution et la composition du compartiment immunitaire évoluent rapidement au fil du temps. Par conséquent, l’information sur la position des cellules immunitaires dans le tissu hépatique est essentielle à la bonne compréhension de leurs fonctions dans la santé et la maladie. Cependant, l’organisation spatiale des cellules immunitaires en réponse à une atteinte hépatique aiguë, ainsi que les conséquences fonctionnelles de leur distribution topographique spécifique, restent mal comprises. Les macrophages hépatiques sont des cellules effectrices clés pendant l’homéostasie et en réponse à des blessures, et sont impliqués dans la pathogenèse de plusieurs maladies du foie. L’hétérogénéité et plasticité des macrophages dans le foie a été exposée avec l’émergence du séquençage de l’ARN, la cytométrie en flux et la cytométrie de masse. Ces techniques ont sensiblement contribué à la compréhension de l’origine, et fonctions des macrophages dans le foie. Cependant, ces technologies impliquent la destruction du tissu pour la préparation de suspension cellulaires ce qui entraîne une perte d’information spatiale et de contexte tissulaire. Par conséquent, la caractérisation spatiale et temporelle des macrophages dans le tissu hépatique pendant l’homéostasie tissulaire, et en réponse à une blessure, fournit une nouvelle information sur la façon dont les macrophages se rapportent aux cellules voisines et leur comportement pendant les réponses immunitaires. Dans la première partie de cette étude, nous avons conçu une stratégie pour le phénotypage spatial des cellules immunitaires hépatiques dans des échantillons de tissus. Cette stratégie combine techniques d'imagerie et l’alignement numérique des images pour surmonter les limitations actuelles du nombre de marqueurs pouvant être visualisés simultanément. En outre, nous avons généré des protocoles pour la quantification automatisée des cellules d’intérêt dans des sections de tissus pour réduire la subjectivité associée à la quantification par inspection visuelle, et pour augmenter la surface et la vitesse de l’analyse. Par conséquent, un plus grand nombre de populations de cellules immunitaires ont été visualisées, quantifiées et cartographiées, et leurs relations spatiales ont été déterminées. Dans la deuxième partie de l’étude, nous avons déterminé la cinétique et la dynamique spatiale des cellules de Kupffer (KCs) et des macrophages dérivés de monocytes (MoMFs) en réponse à une atteinte hépatique aiguë au CCl4, afin de mieux comprendre leurs rôles fonctionnels, et la répartition du travail entre eux. Nous avons constaté que les KC et les MoMFs présentent des différences au niveau de la distribution tissulaire, la morphologie, et la cinétique. En plus, seulement les KCs ont proliféré pour repeupler la population de macrophages résidents pendant la réparation tissulaire. Finalement, nous avons montré que le degré de colocalization de KCs et des MoMFs avec les cellules stellaires est différent. En plus, cette colocalisation varie avec la progression de la réponse immunitaire. Dans l’ensemble, nous avons montré que les KCs et les MoMFs ont des profils spatiaux et temporels différents en réponse à une atteinte hépatique aiguë. Dans l’ensemble, les observations faites dans cette étude suggèrent que le comportement spatial et temporel d’une sous-population donnée de cellules immunitaires est distinct et sous-tend sa capacité à remplir ses fonctions spécifiques pendant la réponse immunitaire. / The immune response is spatially and temporally regulated. Immune cells are part of a larger community of interconnected immune and non-immune cell populations that coordinate their actions mostly through cell-cell intercellular signaling. In the liver, the distribution pattern, and the composition of the immune compartment evolve during an immune response to injury influencing disease pathology, progression, and response to treatment. Hence, information on the location and interacting partners of immune cells in the hepatic tissue is critical for the proper understanding of their functions in health and disease. However, the spatial organization of hepatic resident and infiltrating immune cells in response to acute injury, and the functional consequences of their specific topographical distribution, remain poorly defined. Hepatic macrophages are key effector cells during homeostasis and in response to injury and are involved in the pathogenesis of several liver diseases. The heterogeneity and plasticity of the macrophage compartment in the liver have only recently started to be appreciated with the emergence of RNA sequencing, flow cytometry, and mass cytometry. Detailed transcriptomic and phenotypic profiling have deeply expanded our understanding of macrophage biology. However, these technologies involve tissue disruption with loss of spatial information and tissue context. Therefore, the spatial and temporal profiling of liver macrophages in tissue samples during the steady state, and in response to injury, provide novel information on how the macrophages relate to neighboring cells and their behavior during immune responses. In the first part of this study, we designed a strategy for the spatial phenotyping of hepatic immune cells in tissue samples. This strategy combined serial and sequential labeling, and digital tissue alignment to overcome current limitations in the number of markers that can be simultaneously visualized. In addition, we generated protocols for automated quantification of cells of interest in whole tissue sections which removed the subjectivity associated with quantification by visual inspection and greatly increased the area and the speed of the analysis. As a result, a larger number of immune cell populations were visualized, quantified, and mapped, and their spatial relations were determined in an unbiased manner. In the second part of this study, we monitored the kinetics, and spatial dynamics of resident Kupffer cells (KCs) and infiltrating monocyte-derived macrophages (MoMFs) in response to acute liver injury with CCl4, to gain insight into their functional roles, and the distribution of labor between them. KCs and MoMFs exhibited different tissue distribution patterns and cell morphology, different kinetics, and occupied neighboring but unique microanatomical tissue locations. KCs and MoMFs displayed a different capacity to replenish the macrophage pool upon acute injury, and were differentially related to hepatic stellate cells. Different kinetics and spatial profiles revealed that KCs and MoMFs have distinct spatial signatures and suggest that they perform distinct functions during the wound-healing response to acute liver injury. In summary, we optimized techniques and put together a strategy for the spatial profiling of hepatic immune cells. Then, we used this methodology to profile resident and infiltrating macrophage subpopulations to gain insight into their biology and distinct contribution to healing in response to acute liver injury. Overall, the observations made in this study suggest that the spatial and temporal behavior of a given subpopulation of immune cells underlie its ability to perform its specific functions during the immune response.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.